text: 
  import torch
  from overrides import overrides
  
  from allennlp.common.registrable import Registrable
  
  class SpanExtractor(torch.nn.Module, Registrable):
      """
      Many NLP models deal with representations of spans inside a sentence.
      SpanExtractors define methods for extracting and representing spans
      from a sentence.
  
      SpanExtractors take a sequence tensor of shape (batch_size, timesteps, embedding_dim)
      and indices of shape (batch_size, num_spans, 2) and return a tensor of
      shape (batch_size, num_spans, ...), forming some representation of the
      spans.
      """
      @overrides
      def forward(self, # pylint: disable=arguments-differ
                  sequence_tensor: torch.FloatTensor,
                  span_indices: torch.LongTensor,
                  sequence_mask: torch.LongTensor = None,
                  span_indices_mask: torch.LongTensor = None):
          """
          Given a sequence tensor, extract spans and return representations of
          them. Span representation can be computed in many different ways,
          such as concatenation of the start and end spans, attention over the
          vectors contained inside the span, etc.
  
          Parameters
          ----------
          sequence_tensor : ``torch.FloatTensor``, required.
              A tensor of shape (batch_size, sequence_length, embedding_size)
              representing an embedded sequence of words.
          span_indices : ``torch.LongTensor``, required.
              A tensor of shape ``(batch_size, num_spans, 2)``, where the last
              dimension represents the inclusive start and end indices of the
              span to be extracted from the ``sequence_tensor``.
          sequence_mask : ``torch.LongTensor``, optional (default = ``None``).
              A tensor of shape (batch_size, sequence_length) representing padded
              elements of the sequence.
          span_indices_mask : ``torch.LongTensor``, optional (default = ``None``).
              A tensor of shape (batch_size, num_spans) representing the valid
              spans in the ``indices`` tensor. This mask is optional because
              sometimes it's easier to worry about masking after calling this
              function, rather than passing a mask directly.
  
          Returns
          -------
          A tensor of shape ``(batch_size, num_spans, embedded_span_size)``,
          where ``embedded_span_size`` depends on the way spans are represented.
          """
          raise NotImplementedError
  
      def get_input_dim(self) -> int:
          """
          Returns the expected final dimension of the ``sequence_tensor``.
          """
          raise NotImplementedError
  
      def get_output_dim(self) -> int:
          """
          Returns the expected final dimension of the returned span representation.
          """
          raise NotImplementedError
  